2 Years Postdoc Position Available - 0.8FTE

POSTDOC researcher in the field of SOCIAL URBAN DATA ANALYTICS TO SUPPORT VULNERABLE YOUTH

to be fulfilled in the SocialGlass research group at TU Delft.

Deadline for applications: December 15th 2017

Description

As a part of its research agenda, the LDE Centre for BOLD Cities aims at developing big data solutions and capabilities for city civil servants and policymakers confronted with pressing social issues.

In the context of a recently funded national project (NWA Startimpuls – Big Data voor Jongerenbeleid), the BOLD Cities research team will focus specifically on young people vulnerable to, among other things, labour market insecurity, social exclusion, housing shortages, or anti-social and extremist temptations. In this, the city is a particularly relevant policy space: it is a hyperconnected node that reflects modern societies across the globe and attracts numerous young people. Cities across Europe have expressed concerns about young people disappearing from their radar (‘ghost youth’).

Standard data from city registers, surveys, and focus groups are no longer sufficient to identify the needs and practices of the young population. Social data produced in online collaboration and communication platforms (e.g. Web fora, social media) can provide a valid alternative to traditional data sources, but novel methods able to cope with their heterogeneity, ambiguity, and veracity are needed.

The research activity will be carried out under the supervision of Dr. Alessandro Bozzon and Prof. Liesbet van Zoonen (Erasmus University Rotterdam); and in collaboration with other researchers from the TU Delft and LDE-BOLDCities. The position includes also the supervision of PhD students working on related topics. Participation in education is possible.

Requirements

Candidates are required to have a completed PhD. Specialization and a proven track record are needed in one or more of the following subjects: user modelling, web mining, web science, information retrieval, urban analytics, or related fields.

Preferential consideration will be given to candidates with a genuine passion and commitment to the social issue addressed by the project.

Needed qualifications for candidates include proven research talent, relevant programming skills, experience with programming in the social web, and software development in a team.

Candidates are expected to have an excellent command of English, and good academic writing and presentation skills.

Employment

The position is available for two years, ideally starting in January 2018. The terms of employment are in accordance with the Dutch Collective Labour Agreement for Research Institutes (“CAO-onderzoeksinstellingen”). Expats may apply for a profitable tax ruling, which, if approved by the tax authorities, would mean not having to pay taxes over 30% of your income.

Information and Application

For more information on the position, contact Dr Alessandro Bozzon (a.bozzon@tudelft.nl).

To apply, applicants should send him by email (mention “PD-NWA-YOUTH” in the subject) a curriculum vitae with a list of publications.

The deadline for applications is December 15th, 2017. CVs will be evaluated until the position is filled.

With modern-day cities comes a diverse array of geosocial big data from location-aware technologies. These range from sensing devices, GPS trackers, and satellites through to social media, mobile phones, fleeting transactions, as well as massive government and public data repositories. Together, they provide an exciting reflection of the human landscape. The more knowledge we can derive from geosocial big data, the better we can address real-world urban and regional challenges.

Urban data science is a burgeoning field that has emerged in support of new, geographically-focused computing and data science. It couples theories and methods from urban planning, geography, and computer science to capitalise on the increasing demand for efficient location-based services for cities and metropolitan regions.

The seminar brings together leading experts in the fields of quantitative geography, data science, computational social science, and urban planning, from both academia and practice, to discuss the present and future of (spatial) data science applications in urban and regional contexts.

The corporate smart-city rhetoric is about efficiency, predictability, and security. “You’ll get to work on time; no queue when you go shopping, and you are safe because of CCTV cameras around you”. Well, all these things make a city acceptable, but they don’t make a city great. We are launching goodcitylife.org – a global group of like-minded people who are passionate about building technologies whose focus is not necessarily to create a smart city but to give a good life to city dwellers. The future of the city is, first and foremost, about people, and those people are increasingly networked. We will see how a creative use of network-generated data can tackle hitherto unanswered research questions. Can we rethink existing mapping tools [happy-maps]? Is it possible to capture smellscapes of entire cities and celebrate good odors [smelly-maps]? And soundscapes [chatty-maps]?

Marco Helbich

Geospatial technologies to explore Dutch school commuting

Western health systems are increasingly faced with lifestyle-related diseases such as overweight and obesity. Due to a lack of daily physical activity, being overweight is particularly alarming for the young cohorts and is even more serious because it is expected that a less active behavior is retained in adulthood affecting societal well-being in the long term. To reduce health risk factors the daily journey from residential location to elementary school is seen as a significant contribution to achieve the recommended daily ratio of active movement. Therefore, health agencies show great attention to encouraging children in active transportation modes, i.e. cycling or walking. Despite the apparent importance of the geospatial context and environmental exposures in general and urban design, in particular on cycling and walking, comprehensive knowledge on how these factors are related to children’s active transportation and how they are experienced during transit are lacking. Utilizing GPS data of approximately 100 children in socially deprived neighborhoods located in five Dutch cities, the research objective is to empirically evaluate the impact of urban morphology and design on children’s active mode choice, travel distance, and travel duration. Based on GIS-analysis and the space syntax approach, indices are operationalized to describe urban environments. While controlling for the children’s socio-economic characteristics, the individual and multivariate impact of the build-up environment on mode choice, travel distance, and travel duration is explored through generalized additive (mixed) regressions. Based on these preliminary findings, the talk concludes with recommendations for spatial planning authorities to support a healthier urban living.

11:15 — 11:30 Coffee break I

11:30 — 12:30Alex Singleton

Advances in Geographic Data Science and Urban Analytics

In an era of smartening cities, urban stakeholders are under increasing pressure to derive insight from the mass of new data that are being generated about human dynamics and their contexts. Many new forms of data concern explicitly spatial phenomena, yet many contemporary methods do not account for many of the unique properties of data with geographic attribution. This talk will present an overview of examples from the University of Liverpool Geographic Data Science Lab where we are developing new insight into the dynamics of urban form and human behaviour through the combination of machine learning and explicitly spatial methods.

Chris Brunsdon

Reproducible Methods in Urban Data Science – Ideas and Examples

The idea of reproducible research has gained much recent attention. This is an approach to publishing reports, documents and web sites relating to data analysis in which complete information regarding the data used and the programming scripts used to perform the analysis are encapsulated in a single object. The idea is that third parties can not only read the report (or web site) but they can also reproduce any analytical results or visualisations included in it. This allows the scrutiny of methods used, as well as the adaptation of methods for different data sets or similar but distinct statistical analyses.

In this talk the key ideas and justifications for reproducible research will be discussed, together with a description of a practical implementation of a reproducible research framework based on the R programming language, together with RStudio and RMarkdown and some related tools. In addition to this, some examples of ongoing work using a reproducible paradigm will be given, including an open and reproducible geodemographic classification for the Republic of Ireland, and a related approach to creating dashboards.

12:30 — 13:30 Lunch break

13:30 — 14:30Licia Capra

Data-driven urban planning and policy-making

Urbanization is progressing fast, and it is estimated that by 2050 almost 70% of the total global population will live in cities. This process is expected to bring important advantages, including more efficient running of public services and better living standards for its citizens. However, if not properly managed, it risks aggravating existing issues, such as traffic congestion, environmental pollution, and social inequality.

By acquiring, integrating, and analysing large amounts of heterogeneous data, generated in urban spaces by a diversity of sources, such as sensors, devices, vehicles, buildings, and humans, rich knowledge about the functioning of our cities can be derived and used to improve the quality of life of its residents. Drawing inspirations from different fields, including urban planning and economics, Licia will illustrate the models she has built to understand the nature of urban phenomena, with specific applications to inequalities measurement, urban planning, and sharing economy regulation.

Ger Baron

TBA

14:30 — 15:00 Coffee break II

15:00 — 16:00 Daniel Gatica-Perez

Civic Media, Crowdsourcing, and the Public Good

The engagement of youth in local civic concerns has educational, social, and economic implications. There is an entire open agenda of civic issues that human-centered computing research, in collaboration with young people, can contribute to. This spans data collection about local matters, data analytics, and media creation. I will discuss experiences with mobile crowdsourcing, in which youth make visible urban issues that matter to them. Integrating mobile sensing, audiovisual media, and content analysis, the goal is to enable reflection processes through which discussions to rethink and act on urban issues can emerge. I will argue for the need to work beyond single disciplines and to seize the opportunities that arise from working with communities — both to contribute to the public good and to advance socio-technical approaches involving citizens and cities.

Wouter van Dijk

The potential of data visualization for scientific research

Within scientific projects, a lot of interesting and potentially valuable data is being gathered and analyzed, but often only shared within the project or scientific community. From experience with academic, public and commercial projects, Wouter will share CLEVER°FRANKE’s insights on how to turn that data into data visualization projects that have communicational value. This can be focused on sharing insights, attracting interest in the project or showing the potential of the datasets. The presentation will include a framework on how to classify and ideate types of visualizations with different purposes, as well as examples of past projects.

Civic Media, Crowdsourcing, and the Public Good

Data Visualization for Urban Data Science

Location on the map

]]>http://social-glass.tudelft.nl/ams-seminar-on-social-urban-data-science/feed/0Dr Achilleas Psyllidis gave an invited lecture at the Smart City Conference 2017http://social-glass.tudelft.nl/dr-achilleas-psyllidis-gave-invited-lecture-smart-city-conference-2017/
http://social-glass.tudelft.nl/dr-achilleas-psyllidis-gave-invited-lecture-smart-city-conference-2017/#respondWed, 04 Oct 2017 14:20:18 +0000http://social-glass.tudelft.nl/?p=1687The post Dr Achilleas Psyllidis gave an invited lecture at the Smart City Conference 2017 appeared first on SocialGlass.
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On September 28, 2017, Dr. Achilleas Psyllidis gave an invited lecture at the Smart City Conference 2017 that was held in Eindhoven, the Netherlands.

His lecture, titled “Spatial Data Science for Smart Cities”, emphasised the importance of spatial data science in providing geographically-focused computing solutions that could support planning and decision-making in a smart city context. Achilleas presented the pioneering analytical methods and software tools developed within the Web Information Systems group, Delft Data Science, and the Social Urban Data Lab at the AMS Institute. By capitalising on the explosion of location-aware technologies and geo-tagged data, these methods and tools help solve computationally-intensive problems relating to crowd management, human activity dynamics, and facility siting.

In addition to the lecture, Achilleas gave a live demonstration of the SocialGlass system at the Experience Market, which was running in parallel with the conference.

Introduction

Pixel-based visualizations are a very effective way of displaying large datasets in one view. We use it for displaying traffic information, or more specifically: the speed and flow of vehicles from The Hague to Rotterdam during a period of 9 days. The resulting data visualization shows patterns, trends over time, and anomalies.

This is the third post in a series of three on building a pixel-based visualization of traffic data. Part 1 (Introduction and getting data) can be found here, Part 2 (Positioning Pixels) can be found here. This third post looks at color schemes, explains why rainbow color schemes are in general not suitable for data visualization and why we use it anyway for our pixel plot.

This is especially true when visualizing numerical or ordinal data, i.e. data that has an intrinsic order (like temperature or ratings of restaurants). The colors of the rainbow however have no intuitive order: is red – green – blue the correct order, or is it red – blue – green? The image below shows that working with gradients (color scheme 2) is much more intuitive, it is immediately clear that [b] shows the data ‘in order’ and [a] does not. Note that even the order is more clear in color scheme 2, it is still not clear whether [b] depicts the order from high to low, or from low to high (we will come back to that later).

Two color schemes, one based on ‘rainbow colors’, the other using a gradient. Color scheme 1 has no intuitive ordering, in color scheme 2 the order is immediately visible.

When using the rainbow color scheme for continuous numerical data, there is another issue with the rainbow color map. Imagine that the image below is a visualization of some numerical property with its value changing gradually from high (red) to low (purple). While the value changes gradually, the colors do not! There seem to be ‘bands of colors’ with sharp changes in between, which incorrectly suggest sudden changes in values in the underlying data.

In our case however, we did choose to use (part of) the rainbow color scheme because people easily associate green, yellow, and red to traffic speeds. They intuitively map green to ‘fast’, yellow to ‘slower’, and red to ‘very slow, or standing still’ in analogy to traffic lights. Google traffic uses that same color scheme. The downside of this color scheme is that subtle changes in traffic speed are lost because different shades of green and red are relatively hard to distinguish. So in fact we are using it more as a categorical color scheme than a continuous one.

If we would however focus more on the continuous aspect of our data to show the subtle changes in speed, rather than just the categories ‘slow’, ‘slower’, ‘very slow’, we probably would use a color map that gradually changes its hue and brightness at the same time. A good starting point are the color maps shown below, designed by Stéfan van der Walt and Nathaniel Smith for MatplotLib. The code for generating them can be found here.

Let’s compare a few of the mentioned color scheme’s on our traffic data.

Green-yellow-red

This color scheme takes a subset of the rainbow-color scheme, using only the color range green-yellow-red. We choose those colors since they have a semantic connotation with fast, medium and slow traffic. Since the colors we use have a clear meaning in the traffic domain, this scheme does not suffer from non-intuitive order of colors (as described above). However, it still suffers from non-gradually changing colors and brightness. Within the green values hardly any variation is visible, while the change to yellow is sudden and abrupt.

Rainbow

Here we use the full spectrum of colors. Since we use more colors than in the original color scheme (green-yellow-red), values that are close together will still be distinguishable here, where they may not be distinguishable in the original version. So the rainbow color scheme will show more detail, and subtle changes will be better visible here than in the original version. However, the semantic mapping is bad: red still means very slow, but green is medium speed, and purple is fast. In addition, the rainbow scheme has some inherent problems that we pointed out earlier: no intuitive order of color, and sharp changes between colors.

Plasma

This color scheme does not have the problems of the rainbow scheme. It changes gradually in brightness (from purple to yellow), and does not have sudden changes in color: the hue changes very gradually as well. Changes in speed will therefore be reflected better (i.e. more truthfully). Used on traffic data however, this scheme is semantically less intuitive than the green-yellow-red mapping.

Viridis

The Viridis color scheme has the same advantages as the Plasma scheme: gradually changing hue and brightness, resulting in accurate representation of changes in data. It only ‘travels’ a different route through the color cube, resulting in yellow-green-blue instead of yellow-orange-purple. So whether to choose Plasma or Viridis is mainly a matter of taste, unless one of the color schemes better matches the association between color and data that users may have.

The color schemes that seems to be most suitable for our traffic data are the green-yellow-red scheme (because of its clear connotation with fast, medium, and slow) and the plasma/viridis color schemes (because they show subtle changes better than the rainbow-color-based scheme’s, and are therefore a more accurate representation of the underlying data). The image below shows the Plasma color scheme (top) and the rainbow-based green-yellow-red (bottom). Some changes in speed are better visible in the Plasma scheme, for instance in the 12:00 block at the far left. As you may have guessed from the first blog-post in this series, we choose to use the Plasma color scheme, for its more accurate representation of the data.

Comparison between rainbow-based color scheme (top) with Plasma color scheme (bottom). Subtle changes in speed are better visible in the latter, while the top scheme has a more intuitive mapping between speed and color (green is fast, red is slow).

Conclusion

This series of posts showed how traffic data can be visualized with pixel-based visualization techniques. We discussed the motive behind this type of visualization and how we retrieved the data (from the NDW, see post 1), It discussed the most optimal way of arranging pixels (a modified Z-order, see post 2) and it showed why we choose the yellow-orange-purple-blue color scheme (this post). Whether this is the best way to visualize remains a topic of further investigation, and requires user tests. These tests will reveal whether users do indeed see interesting patterns and outliers that they would not see with other types of visualization, or that would not be detected with other (e.g. automatic) methods.

Pixel-plot of traffic data over 9 days on the A13 to Rotterdam. Color indicates speed, rectangle-size indicates flow. Circles highlight speeding cars (driving faster than 150km/h). Click image to open full size version in a new tab.

Alessandro Bozzon has been selected for the IBM Faculty Award, which is a competitive worldwide program intended to foster collaboration between researchers at leading universities worldwide and those in IBM research.

Alessandro Bozzon received the award in the section CognitiveComputing and IoT for his work on Enterprise Crowdsourcing performed in collaboration with the IBM Benelux Center for Advanced Studies.

To qualify for this internationally competitive award, the nominee must be a full-time professor at an accredited university that has a Ph.D. or MBA program in the nominee’s field. Candidates must have an outstanding reputation for contributions in their field or, in the case of junior faculty, show unusual promise.

The IBM Faculty Award will be used to support curriculum innovation in strategic disciplines, which will include the development of a new professional masterclass on The Internet of Everything and Everyone, and other initiatives related to crowd computing

]]>http://social-glass.tudelft.nl/alessandro-bozzon-selected-ibm-2017-faculty-award/feed/0Alessandro Bozzon speaking at the first HComp-NL Symposiumhttp://social-glass.tudelft.nl/alessandro-bozzon-speaking-at-the-first-hcomp-nl-symposium/
Fri, 15 Sep 2017 22:19:53 +0000http://social-glass.tudelft.nl/?p=1551The post Alessandro Bozzon speaking at the first HComp-NL Symposium appeared first on SocialGlass.
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In the context of the first anniversary of the inaugural lecture of prof. dr. Lora Aroyo entitled “Data Science with Humans in the Loop“, Dr. Alessandro Bozzon gave a talk entitled “Weaving the Internet of People and Things for Intelligent Cities“.

The event has been organized by the Human Computation Community in the Netherlands (HComp-NL), supported by 4TU-NIRICT. The event was organized in two sessions. The first session took place on Thursday, September 14th and was specifically aimed at PhD students and postdocs, providing practical experiences on how various forms of crowdsourcing, nichesourcing, human computation and citizen science can help gather, harness and capture human knowledge at scale and thus ultimately improve machine-based systems. The session featured presentations by Alessandro Bozzon and Anca Dumitrache. The second session of the symposium presented a number of international researchers from academia and industry all working in different fields of human computation and user-generated content, including:Elena Simperl (University of Southampton), Zoltán Szlávik (IBM Benelux), and Chris Welty (Google Research).

The presentation from Alessandro Bozzon is now available on SlideShare.

]]>Pixel-based visualization of traffic data – part 2/3http://social-glass.tudelft.nl/pixel-based-visualization-of-traffic-data-part-23/
http://social-glass.tudelft.nl/pixel-based-visualization-of-traffic-data-part-23/#respondWed, 06 Sep 2017 11:55:29 +0000http://social-glass.tudelft.nl/?p=422Introduction Pixel-based visualizations are a very effective way of displaying large datasets in one view. We use it for displaying traffic information, or more specifically: the speed and flow of vehicles from The Hague to Rotterdam during a period of 9 days. The resulting data visualization shows patterns, trends over time, and anomalies. This is the […]

Pixel-based visualizations are a very effective way of displaying large datasets in one view. We use it for displaying traffic information, or more specifically: the speed and flow of vehicles from The Hague to Rotterdam during a period of 9 days. The resulting data visualization shows patterns, trends over time, and anomalies. This is the second post in a series of three on building a pixel-based visualization of traffic data. Part 1 (Introduction and getting data) can be found here, Part 3 (Harmful Rainbows) will follow. This second post looks at pixel positioning in pixel-based visualizations. Rather than putting one pixel next to the other in a simple left-to-right fashion we look into more sophisticated (and better) ways of putting pixels on the screen.

Where to put the pixels

We use a slightly modified approach as compared to the one described in Keim’s paper in the sense that we vary the size of the pixels (actually: rectangles), while in his paper all pixels have the same size. Varying size allows for displaying not only speed (color of the pixels) but also flow (size of the pixels). The position of the pixels encodes time: inside each strip, we group pixels per hour from left to right. Positioning pixels in pixel-based visualizations is not trivial. Ideally, you should aim for the preservation of locality, which means in our case that two pixels should be close to each other on the screen if they represent two measurements that are close to each other in time. This is easy in one dimension: just put all pixels next to each other. In the two-dimensional situation dictated by a computer screen, this is less trivial.

Imagine putting all pixels next to each other, from left to right, until the end of the screen is reached, then start over again, from left to right. This works fine horizontally: pixels next to each in position, are also next to each other ‘in time’. Vertically, it does not work very well: pixels above each other are close in position, but very far apart in time.

Naive approach in positioning pixels. Pixels vertically close in space represent values that are far apart in time.

A much better approach is the Z-order curve, that preserves locality much better. This curve is ‘zig-zagging’ in a Z-shape as shown in the image below:

An arrangement of pixels following the Z-order curve.

A closer look reveals that the Z-order curve is ordering the cells in a Z shape in a hierarchical fashion. The same Z shape is visible on multiple levels. On the lowest level pixels are arranged in a Z shape giving blocks of 2×2 pixels. These blocks are, in turn, arranged in a Z shape, resulting in blocks of 4 x 4 pixels which in turn are arranged in a Z shape, etc. (see image below).

Z order shape is a hierarchical ordering, following the 2 x 2 Z shape on all levels.

This Z order is a logical candidate for our pixel-based visualization of traffic data. The main disadvantage, however, is that the resulting blocks do not have a clear meaning. The blocks do not correspond to the time concepts (‘quarter’, ‘hour’, ‘day’) that we are used to. Instead, the Z order gives blocks of 4, 16 and 64 pixels. These numbers are hard to match to logical concepts. So instead we use a modified Z order, where instead of blocks of 2×2, we choose the size of the blocks in such a way that they map to logical concepts. Since each pixel represents one minute, we choose a block size of 15 on the lowest level (representing a quarter of an hour), and a block size of 2 x 2 quarters on the next level, representing one hour. On the highest level, we have a block size of 24 x 1, representing 7 hours, corresponding to one day.

Modified Z order curve that maps to logical time concepts ‘quarter’, ‘hour’ and ‘day’.

Modified Z ordering in our pixel-plot.

Now that we know where to put the pixels, the last post of this series will discuss their color. Stay tuned!

]]>http://social-glass.tudelft.nl/pixel-based-visualization-of-traffic-data-part-23/feed/0SocialGlass is featured on Het Paroolhttp://social-glass.tudelft.nl/socialglass-is-featured-on-het-parool/
http://social-glass.tudelft.nl/socialglass-is-featured-on-het-parool/#respondSat, 19 Aug 2017 22:10:01 +0000http://social-glass.tudelft.nl/?p=1545Het Parool (the main Amsterdam daily newspaper) features an interview with Dr. Alessandro Bozzon about SocialGlass and the research work of the team. The August edition of De Ingenieur features an extensive interview of Dr Achilleas Psyllidis about SocialGlass. The article is the fifth in a series devoted to the problem of crowdedness in the […]

]]>Het Parool (the main Amsterdam daily newspaper) features an interview with Dr. Alessandro Bozzon about SocialGlass and the research work of the team.
The August edition of De Ingenieur features an extensive interview of Dr Achilleas Psyllidis about SocialGlass.

The article is the fifth in a series devoted to the problem of crowdedness in the city of Amsterdam. It describes how the SocialGlass approach could be used to better understand and forecast the flows of people in cities. It further outlines how the SocialGlass research team envisions new ways for cities to communicate with their citizens and visitors; for instance, using new recommendation technologies and conversational agents.

]]>http://social-glass.tudelft.nl/socialglass-is-featured-on-het-parool/feed/0SocialGlass is featured in the August edition of De Ingenieurhttp://social-glass.tudelft.nl/socialglass-is-featured-in-the-august-edition-of-de-ingenieur/
http://social-glass.tudelft.nl/socialglass-is-featured-in-the-august-edition-of-de-ingenieur/#respondFri, 11 Aug 2017 09:01:22 +0000http://social-glass.tudelft.nl/?p=1527The August edition of De Ingenieur features an extensive interview of Dr Achilleas Psyllidis about SocialGlass. The article describes how SocialGlass paves the way for smarter cities, by offering a pioneering toolbox that helps governments, planners, and wider communities harness the power of urban big data to better understand and forecast the dynamics of cities. […]

]]>The August edition of De Ingenieur features an extensive interview of Dr Achilleas Psyllidis about SocialGlass.

The article describes how SocialGlass paves the way for smarter cities, by offering a pioneering toolbox that helps governments, planners, and wider communities harness the power of urban big data to better understand and forecast the dynamics of cities. It further outlines how the system facilitates the combination of insights derived from sources as diverse as social media, sensors, GPS devices, LoRa networks, open data portals, and crowdsourcing campaigns. The article puts emphasis on the various real-life applications of the system in urban contexts, with special focus on crowd management during SAIL 2015 in Amsterdam.

]]>http://social-glass.tudelft.nl/socialglass-is-featured-in-the-august-edition-of-de-ingenieur/feed/0SocialGlass is nominated for the Accenture Innovation Awards 2017http://social-glass.tudelft.nl/socialglass-is-nominated-for-the-accenture-innovation-awards-2017/
http://social-glass.tudelft.nl/socialglass-is-nominated-for-the-accenture-innovation-awards-2017/#respondTue, 08 Aug 2017 09:43:39 +0000http://social-glass.tudelft.nl/?p=1519SocialGlass is nominated for the Accenture Innovation Awards 2017 under the Perfect Cities theme. “Perfect Cities” focus on urban innovations and smart city technologies that help transform city life. Besides the main award, SocialGlass also competes for the Public Prize, which is based on a social voting procedure. Therefore, your vote is important. You may […]

]]>SocialGlass is nominated for the Accenture Innovation Awards 2017 under the Perfect Cities theme.
“Perfect Cities” focus on urban innovations and smart city technologies that help transform city life.

Besides the main award, SocialGlass also competes for the Public Prize, which is based on a social voting procedure.

Therefore, your vote is important. You may cast your vote for SocialGlass here.